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I will fine tune llama or qwen llms on your dataset with lora
AI Engineer, RAG Systems, LLM FineTuning, LangChain Python
I build production-ready AI systems — RAG chatbots, fine-tuned
LLMs, and intelligent Python applications.
My recent work:
→ FinSight Pakistan: Hybrid-search RAG system over 916K words - 75.3%
relev...
About this Gig
Adapt an open-weight LLM to your task with custom LoRA fine-tuning in Python and Hugging Face. I work with Llama, Qwen, Mistral, Gemma and Phi, depending on model compatibility and GPU resources.
Use cases include instruction following, summarization, classification and domain-specific responses.
Choose your package:
- Basic: supervised fine-tuning with LoRA on up to 1,000 prepared samples; adapters and source code.
- - Standard: SFT on up to 5,000 samples, dataset preparation and evaluation metrics.
- - Premium: SFT plus DPO alignment, model card and Hugging Face deployment for the agreed scope.
My EdgeAlign project used SFT and DPO on Qwen3-0.6B with LoRA. Results depend on the dataset and task; improvement is assessed against a baseline.
Before ordering, send a data sample, sample count, task description, preferred model and GPU details. DPO requires suitable preference data. We will confirm model size, training scope, evaluation and compute or hosting costs before work begins.
Programming Language:
Python
Data Type:
Text
•
Tabular Data
AI Engine:
Llama
•
Falcon
•
Langchain
•
Other
My Portfolio
FAQ
What format does my dataset need to be in?
Ideally JSONL with instruction/input/output fields, or CSV. I can help you reformat raw data into the right structure if needed - just share a sample first.
What if my dataset is small (under 500 samples)?
Small datasets are fine for LoRA fine-tuning. Message me with your sample count and I will advise on the best approach.
Will I own the fine-tuned model?
Yes - you get full ownership of the LoRA adapters and all code. Premium package includes deployment to your own HuggingFace account.
Can you fine-tune GPT-4 or Claude?
No - those are closed-source models and cannot be fine-tuned this way. I work with open-source models on HuggingFace (LLaMA, Qwen, Mistral, Gemma, Phi etc.)
What evaluation metrics do you provide?
BLEU score, BERTScore, training/validation loss curves, and sample output comparisons against baseline. Premium package includes a full ablation study.
